This invention relates generally to read-detection in solid-state storage devices (SSSDs), and more particularly to methods and apparatus for detecting codewords stored in multi-level memory cells on readout of solid-state memory.
In solid-state memory such as flash memory and phase change memory (PCM), the fundamental storage unit (the “cell”) can be set to a number of different states, or “levels”, which exhibit different electrical characteristics. These different levels can be used to store information. To readout stored information, cell-level is detected via measurements which exploit the differing electrical characteristics to differentiate between different levels. In so-called “single-level cell” (SLC) devices, the memory cells can be set to only two levels and so can record only binary values. Other devices have so-called “multi-level cells” which can be set to q different levels, where q>2. Multi-level NOR flash memories, for instance, can store 4 levels, i.e., 2 bits, per cell. Multi-level cell (MLC) NAND flash memory chips that can store 3 bits of data per single flash cell using 25 nm process technology are currently available. Storage of 2 bits per cell in PCM chips has also been demonstrated.
When writing information to multi-level cells, each cell can be used to store a qary symbol with each of the q possible symbol values being represented by a different cell level. On readout of multi-level cells, the read signal level is compared with a set of reference signal levels indicative of the q cell-levels in order to determine which level each cell is set to and thus detect the stored symbol value. However, a problem in multi-level SSSDs is that the physical quantity measured during cell readout, such as electrical resistance in PCM devices, is liable to drift. In particular, the electrical resistance of PCM cells drifts upwards with time in a stochastic manner. This drift can be data-dependent, i.e., may vary for different cell levels. As another example, in flash memory cells the physical quantity measured is the transistor's threshold voltage and this drifts upwards as a function of the number of write/erase cycles the cell is subjected to. For any given stored symbol value and hence cell level, therefore, the actual read signal level obtained on cell-readout is variable.
Drift is a serious problem for multi-level storage in that it severely compromises reliability. The readback values of neighboring levels may interfere over time, due to upward drift of the lower level towards the upper level, causing detection errors. The closer the initial spacing between levels, the more susceptible they are to drift. Hence packing higher numbers of levels per memory cell becomes more difficult due to the increased likelihood of error during detection. On the other hand, packing more bits per cell is a crucial requirement for all memory technologies, being the best known way of reducing manufacturing cost per bit. Hence, in situations like this where the read signal level distributions for cell-levels are varying, the reference signal levels used for level detection need to be varied as well, e.g., with time, or with the number of write cycles, etc. Reliable estimation of the reference signal levels is crucial to reliable readback performance.
One approach to the above problem uses training data derived from a pool of reference memory cells for drift estimation. Known information is written to some of these reference cells each time a block of user data is written to memory. These reference cells are then read whenever the user file is read, and the reference cell readings are used to derive estimates for the changing reference signal levels used for detection of the user data. Use of a large amount of this training data significantly reduces storage capacity, while reducing the amount of training data reduces accuracy of level estimation. There is also a penalty in terms of controller complexity and latency due to readout of the extra cells, as well as issues, e.g., wear-leveling issues, related to management of the pool of reference cells. Further, since drift is a statistical phenomenon and there is significant variability between cells in a memory array, reference cells may not be representative and the effectiveness of this approach may vary substantially with time and over different portions of the memory array.
Model-based drift cancellation techniques provide another approach. These seek to model drift based on key parameters such as temperature, time and wear, and compensate accordingly. It is, however, difficult to obtain an accurate cell history for the key parameters. There are also fluctuations from cell to cell and there is no well-established analytical model available for short-term drift.
Our copending European Patent Application no. 11183336.4, filed 29 Sep. 2011, discloses a drift-resistant technique for read-detection of permutation-based codes in multi-level SSSDs. The codes in question are length-N, qary-symbol codes, whereby each codeword has N symbols and each symbol can take one of q symbol values. Each symbol is recorded in a respective q-level cell by setting the cell to a level dependent on the qary symbol value. The detection system exploits the property of permutation-based codes that all codewords are permutations of a known set of N-symbol vectors, e.g., the so-called “initial vectors” for a union of permutation codes. Memory cells are read in batches to obtain read signals corresponding to a group of codewords. Each read signal has N signal components corresponding to respective symbols of a codeword, and these components are ordered according to signal level to obtain an ordered read signal for each codeword. Components of these ordered read signals are related to symbols of the known set of initial vectors via a process which involves averaging ordered read signals and relating the averaged signal components to symbol values using predefined probabilities of occurrence of different symbol values at different symbol positions as derived from the initial vectors. This reduces the problem of finding the q drifted reference signal levels to the problem of solving an over-determined system of N linear equations for the q unknown reference levels. In this way, estimates are obtained for the reference signal levels for the q-level cells in the current batch, and these reference levels are then used in codeword detection for the batch. Unlike the training-data approach, this is a self-adaptive technique which uses the actual cells storing encoded user data to estimate the reference levels for those cells on readback, thereby accounting for drift effects on a dynamic basis. The technique is also robust and lends itself to simple, fast decoder implementation. However, level-estimation performance can be variable with certain conditions, e.g., depending on the particular code employed, and small batch sizes in particular can adversely affect reliability, especially for “inner” levels of the series of q memory cell levels.